Picking some countries that have significant changes over the past 6 years. Peru
#scores data
scores <- read_csv("../scores_2018-11-09.csv") %>%
filter(goal == "FIS")
#region details to help us filter by country name rather than ID
rgn_deets <- read_csv("https://raw.github.com/OHI-Science/ohiprep_v2018/gh-pages/globalprep/supplementary_information/v2018/rgn_details.csv")
#pull out some specific countries
ctry_data <- scores %>%
left_join(rgn_deets, by = c("region_id" = "rgn_id")) %>%
filter(rgn_nam %in% c("Peru", "Ile Europa", "Amsterdam Island and Saint Paul Island", "Ile Tromelin", "Maldives", "Sierra Leone"))Plot these countries
score <- ctry_data %>%
filter(dimension == "score")
ggplot(score, aes(x = year, y = score, color = rgn_nam)) +
geom_line() +
theme_bw() +
labs(y = "FIS score") +
theme(legend.title=element_blank())It’s interesting that Ile Europa, Ile Tromelin, Amsterdam Island & St. Paul Island maintain the same pattern. Maldives is slightly different but still a steep decline after 2014. Peru and Sierra Leone are strikingly different.
t <- ctry_data %>%
filter(dimension %in% c("pressures", "status", "resilience"))
ggplot(t, aes(x = year, y = score, color = dimension)) +
geom_line() +
theme_bw() +
facet_wrap(~rgn_nam) +
theme(legend.title=element_blank())It looks like pressures and resilience are not having a big impact on these status values. Next things to look at is catch over time and stock scores with proportional catch over time.
Looking at b/bmsy and catch data
bbmsy <- read_csv("~/github/ohi-global/eez/layers/fis_b_bmsy.csv") %>%
filter(rgn_id %in% unique(ctry_data$region_id)) %>%
mutate(group =
case_when(
bbmsy < 0.8 ~ "overfished",
bbmsy >= 0.8 & bbmsy <= 1.45 ~ "fully exploited",
bbmsy > 1.45 ~ "underfished"
))
catch <- read_csv("~/github/ohi-global/eez/layers/fis_meancatch.csv") %>%
filter(rgn_id %in% unique(ctry_data$region_id)) %>%
separate(stock_id_taxonkey, into = c("stock_id", "taxonkey"), sep = "_(?=[:digit:])")
combine <- bbmsy %>%
full_join(catch) %>%
group_by(rgn_id, group, year) %>%
summarize(catch = sum(mean_catch, na.rm=T)) %>%
left_join(rgn_deets) %>%
select(year, rgn_nam, group, catch) %>%
distinct() %>%
mutate(status = ifelse(is.na(group), "no status", group)) %>%
filter(year > 2008)
ggplot(combine, aes(year, catch)) +
geom_area(aes(fill = status)) +
facet_wrap(~rgn_nam, scales = "free") +
scale_fill_manual(breaks = c("fully exploited", "no status", "overfished", "underfished"),
values=c("darkblue", "orange", "darkred", "darkgreen")) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))What stocks are making up Sierra Leone’s catch?
sl <- catch %>%
filter(rgn_id == 96,
year > 2008) %>%
left_join(bbmsy) %>%
group_by(year) %>%
mutate(catch_prop = mean_catch/sum(mean_catch))
plotly::ggplotly(ggplot(sl, aes(year, catch_prop)) +
geom_area(aes(fill = stock_id)) +
theme_bw() +
theme(legend.position=""))It looks like Ethmalosa fimbriata - 34 makes up a significant portion of the catch! Almost 60% of catch. This is a Bonga Shad
shad_bbmsy <- bbmsy %>%
filter(stock_id == "Ethmalosa_fimbriata-34")
ggplot(shad_bbmsy, aes(x = year, y = bbmsy)) +
geom_line() +
theme_bw() +
geom_hline(yintercept = 1.45, color = "darkgreen", lty = "dashed") +
ylab("B/Bmsy") +
xlab("Year") +
ggtitle("Bonga Shad FAO area 34")This seems to be a large fishery for West Africa (FAO). And the status has been getting closer to 1 so it improves the score. The dashed green line shows where we define underexploted (>1.45) and fully exploited (<1.45) which explains the dramatic shift in the plot.
Let’s see what’s going on with Peru
peru <- catch %>%
filter(rgn_id == 138,
year > 2008) %>%
left_join(bbmsy) %>%
group_by(year) %>%
mutate(catch_prop = mean_catch/sum(mean_catch))
plotly::ggplotly(ggplot(peru, aes(year, catch_prop)) +
geom_area(aes(fill = stock_id)) +
theme_bw() +
theme(legend.position=""))Again we have a single fishery making up almost 68% of the total catch. This isn’t surprising as Peruvian anchoveta is one of the largest fisheries in the world.
anch_bbmsy <- bbmsy %>%
filter(stock_id == "Engraulis_ringens-87") %>%
filter(year > 2007)
ggplot(anch_bbmsy, aes(x = year, y = bbmsy)) +
geom_line() +
theme_bw() +
geom_hline(yintercept = 1.45, color = "darkgreen", lty = "dashed") +
labs(y = "B/Bmsy",
x = "Year",
title = "Engraulis ringens FAO area 87")Let’s look at the islands. Ile Europa and Ile Tromelin are geographically close, near Madagascar.
Iles <- catch %>%
filter(rgn_id %in% c(35,36),
year > 2008) %>%
left_join(bbmsy) %>%
group_by(year, rgn_id) %>%
mutate(catch_prop = mean_catch/sum(mean_catch)) %>%
left_join(rgn_deets)
plotly::ggplotly(ggplot(Iles, aes(year, catch_prop)) +
geom_area(aes(fill = stock_id)) +
theme_bw() +
theme(legend.position="") +
facet_wrap(~rgn_nam))Ok it looks like Tuna is a significant portion of catch for these islands.
ile_catch <- Iles %>%
filter(catch_prop > 0.1,
!is.na(bbmsy))
ggplot(ile_catch, aes(x = year, y = bbmsy, color = stock_id)) +
geom_line() +
theme_bw() +
labs(y = "B/Bmsy",
x = "Year",
ggtitle = "Tuna status for FAO 51") +
theme(legend.title=element_blank())Let’s look at the Maldives
maldives <- catch %>%
filter(rgn_id == 39,
year > 2008) %>%
left_join(bbmsy) %>%
group_by(year) %>%
mutate(catch_prop = mean_catch/sum(mean_catch)) %>%
left_join(rgn_deets)
plotly::ggplotly(ggplot(maldives, aes(year, catch_prop)) +
geom_area(aes(fill = stock_id)) +
theme_bw() +
theme(legend.position=""))Again Katsuwonus pelamis (Skipjack Tuna) is majority of catch.
Is Amsterdam Island also highly dependant on Skipjack? It’s much more subpolar (near Antarctic)
ams <- catch %>%
filter(rgn_id == 92,
year > 2008) %>%
left_join(bbmsy) %>%
group_by(year) %>%
mutate(catch_prop = mean_catch/sum(mean_catch)) %>%
left_join(rgn_deets)
plotly::ggplotly(ggplot(ams, aes(year, catch_prop)) +
geom_area(aes(fill = stock_id)) +
theme_bw() +
theme(legend.position=""))This time is Thunnus albacares (Yellowfin tuna) which has a similar trajectory to Skipjack.